Statistical analysis of financial time series and risk management

The dissertation studies the dynamic of volatility, skewness, and value at risk for financial returns. It contains three topics.;The first one is the asymptotic properties of the conditional skewness model for asset pricing. We start with a simple consumption-based asset pricing model, and make a connection between the asset pricing model and the regularity conditions for a quantile regression. We prove that the quantile regression estimators are asymptotically consistent and normally distributed under certain assumptions for the asset pricing model.;The second one is about dynamic quantile models for risk management. We propose a financial risk model based on dynamic quantile regressions, which allows us to estimate conditional volatility and skewness jointly. We compare this approach with ARCHtype models by simulation. We also propose a density fitting approach by matching conditional quantiles and parametric densities to obtain the conditional distributions of returns.;The third one is a simulation study of a consumption based asset pricing model. We show that larger returns and Sharp ratio can be obtained by introducing conditional asymmetry in the asset pricing model.